Indigenous Communities, Cooperation, and Communication: Taking Experiments to the Field
Bibliographic record
Abstract
Much experimental research has been conducted in laboratory settings on human behavior related to public goods, common-pool resources, and other social dilemmas. These studies have shown that when subjects are anonymous and not allowed to communicate, they tend not to cooperate. However, to the surprise of game theorists, simply allowing subjects to communicate in a laboratory setting enables them to achieve far more cooperative outcomes. The replication of the experiment in laboratory settings in multiple countries as well as in some initial field experiments has only confirmed this important finding. However, while carefully conducted laboratory experiments do have strong internal validity, external validity requires further research beyond the initial field experiments that researchers have begun to conduct. In this paper, we report on a series of common-pool-resource field experiments conducted in eight indigenous communities in India that have very long traditions of shared norms and mutual trust. We used two experimental designs in all eight villages: a "no-communication" game where no one was allowed verbal or written communication and a "communication game" in which the same five participants were allowed to communicate with each other at the beginning of each round before making their decisions. The findings from these field experiments are substantially different from the findings of similar experiments conducted in experimental laboratories. Subjects tended to cooperate in the first design even in the absence of communication. Our findings suggest that the shared norms in these indigenous communities are so deeply embedded that communication is not essential to arrive at cooperative decisions. However, communication does homogenize group and individual outcomes so that communities that are overly cooperative tend to reduce cooperation slightly while those showing small deviations in the other direction move toward the optimal solution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".